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                        机器学习（常用科学计算库的使用）基础定位、目标
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                        人工智能主要分支
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                        机器学习算法分类
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                            <b>1.7.</b>
                        
                        Azure机器学习模型搭建实验
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                        库的安装
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                        Matplotlib
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                        Numpy
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                        Numpy的优势
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                        N维数组-ndarray
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                        高级处理-缺失值处理
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                        高级处理-数据离散化
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                        高级处理-合并
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                        高级处理-交叉表与透视表
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                        完整机器学习项目的流程
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<li><strong>&#x534A;&#x76D1;&#x7763;&#x5B66;&#x4E60;&#x8BAD;&#x7EC3;&#x65B9;&#x5F0F;</strong></li>
</ul>
<p><img src="images/&#x534A;&#x76D1;&#x7763;&#x5B66;&#x4E60;2.png" alt="image-20190218141154743"></p>
<h2 id="4-&#x5F3A;&#x5316;&#x5B66;&#x4E60;">4 &#x5F3A;&#x5316;&#x5B66;&#x4E60;</h2>
<ul>
<li>&#x5B9A;&#x4E49;&#xFF1A;<ul>
<li>&#x5B9E;&#x8D28;&#x662F;make decisions &#x95EE;&#x9898;&#xFF0C;&#x5373;&#x81EA;&#x52A8;&#x8FDB;&#x884C;&#x51B3;&#x7B56;&#xFF0C;&#x5E76;&#x4E14;&#x53EF;&#x4EE5;&#x505A;&#x8FDE;&#x7EED;&#x51B3;&#x7B56;&#x3002;</li>
</ul>
</li>
</ul>
<p>&#x4E3E;&#x4F8B;&#xFF1A;</p>
<p>&#x5C0F;&#x5B69;&#x60F3;&#x8981;&#x8D70;&#x8DEF;&#xFF0C;&#x4F46;&#x5728;&#x8FD9;&#x4E4B;&#x524D;&#xFF0C;&#x4ED6;&#x9700;&#x8981;&#x5148;&#x7AD9;&#x8D77;&#x6765;&#xFF0C;&#x7AD9;&#x8D77;&#x6765;&#x4E4B;&#x540E;&#x8FD8;&#x8981;&#x4FDD;&#x6301;&#x5E73;&#x8861;&#xFF0C;&#x63A5;&#x4E0B;&#x6765;&#x8FD8;&#x8981;&#x5148;&#x8FC8;&#x51FA;&#x4E00;&#x6761;&#x817F;&#xFF0C;&#x662F;&#x5DE6;&#x817F;&#x8FD8;&#x662F;&#x53F3;&#x817F;&#xFF0C;&#x8FC8;&#x51FA;&#x4E00;&#x6B65;&#x540E;&#x8FD8;&#x8981;&#x8FC8;&#x51FA;&#x4E0B;&#x4E00;&#x6B65;&#x3002;</p>
<p>&#x5C0F;&#x5B69;&#x5C31;&#x662F; <strong>agent</strong>&#xFF0C;&#x4ED6;&#x8BD5;&#x56FE;&#x901A;&#x8FC7;&#x91C7;&#x53D6;<strong>&#x884C;&#x52A8;</strong>&#xFF08;&#x5373;&#x884C;&#x8D70;&#xFF09;&#x6765;&#x64CD;&#x7EB5;<strong>&#x73AF;&#x5883;</strong>&#xFF08;&#x884C;&#x8D70;&#x7684;&#x8868;&#x9762;&#xFF09;&#xFF0C;&#x5E76;&#x4E14;&#x4ECE;<strong>&#x4E00;&#x4E2A;&#x72B6;&#x6001;&#x8F6C;&#x53D8;&#x5230;&#x53E6;&#x4E00;&#x4E2A;&#x72B6;&#x6001;</strong>&#xFF08;&#x5373;&#x4ED6;&#x8D70;&#x7684;&#x6BCF;&#x4E00;&#x6B65;&#xFF09;&#xFF0C;&#x5F53;&#x4ED6;&#x5B8C;&#x6210;&#x4EFB;&#x52A1;&#x7684;&#x5B50;&#x4EFB;&#x52A1;&#xFF08;&#x5373;&#x8D70;&#x4E86;&#x51E0;&#x6B65;&#xFF09;&#x65F6;&#xFF0C;&#x5B69;&#x5B50;&#x5F97;&#x5230;<strong>&#x5956;&#x52B1;</strong>&#xFF08;&#x7ED9;&#x5DE7;&#x514B;&#x529B;&#x5403;&#xFF09;&#xFF0C;&#x5E76;&#x4E14;&#x5F53;&#x4ED6;&#x4E0D;&#x80FD;&#x8D70;&#x8DEF;&#x65F6;&#xFF0C;&#x5C31;&#x4E0D;&#x4F1A;&#x7ED9;&#x5DE7;&#x514B;&#x529B;&#x3002;</p>
<p>&#x4E3B;&#x8981;&#x5305;&#x542B;&#x4E94;&#x4E2A;&#x5143;&#x7D20;&#xFF1A;agent, action, reward, environment, observation&#xFF1B;</p>
<p><img src="images/&#x5F3A;&#x5316;&#x5B66;&#x4E60;&#x793A;&#x4F8B;.png" alt="image-20190218141450790"></p>
<p>&#x5F3A;&#x5316;&#x5B66;&#x4E60;&#x7684;&#x76EE;&#x6807;&#x5C31;&#x662F;<strong>&#x83B7;&#x5F97;&#x6700;&#x591A;&#x7684;&#x7D2F;&#x8BA1;&#x5956;&#x52B1;</strong>&#x3002;</p>
<p>&#x76D1;&#x7763;&#x5B66;&#x4E60;&#x548C;&#x5F3A;&#x5316;&#x5B66;&#x4E60;&#x7684;&#x5BF9;&#x6BD4;</p>
<table>
<thead>
<tr>
<th></th>
<th><strong>&#x76D1;&#x7763;&#x5B66;&#x4E60;</strong></th>
<th><strong>&#x5F3A;&#x5316;&#x5B66;&#x4E60;</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td>&#x53CD;&#x9988;&#x6620;&#x5C04;</td>
<td>&#x8F93;&#x51FA;&#x7684;&#x662F;&#x4E4B;&#x95F4;&#x7684;&#x5173;&#x7CFB;&#xFF0C;&#x53EF;&#x4EE5;&#x544A;&#x8BC9;&#x7B97;&#x6CD5;&#x4EC0;&#x4E48;&#x6837;&#x7684;&#x8F93;&#x5165;&#x5BF9;&#x5E94;&#x7740;&#x4EC0;&#x4E48;&#x6837;&#x7684;&#x8F93;&#x51FA;&#x3002;</td>
<td>&#x8F93;&#x51FA;&#x7684;&#x662F;&#x7ED9;&#x673A;&#x5668;&#x7684;&#x53CD;&#x9988; reward   function&#xFF0C;&#x5373;&#x7528;&#x6765;&#x5224;&#x65AD;&#x8FD9;&#x4E2A;&#x884C;&#x4E3A;&#x662F;&#x597D;&#x662F;&#x574F;&#x3002;</td>
</tr>
<tr>
<td>&#x53CD;&#x9988;&#x65F6;&#x95F4;</td>
<td>&#x505A;&#x4E86;&#x6BD4;&#x8F83;&#x574F;&#x7684;&#x9009;&#x62E9;&#x4F1A;<strong>&#x7ACB;&#x523B;&#x53CD;&#x9988;&#x7ED9;&#x7B97;&#x6CD5;</strong>&#x3002;</td>
<td>&#x7ED3;&#x679C;<strong>&#x53CD;&#x9988;&#x6709;&#x5EF6;&#x65F6;</strong>&#xFF0C;&#x6709;&#x65F6;&#x5019;&#x53EF;&#x80FD;&#x9700;&#x8981;&#x8D70;&#x4E86;&#x5F88;&#x591A;&#x6B65;&#x4EE5;&#x540E;&#x624D;&#x77E5;&#x9053;&#x4EE5;&#x524D;&#x7684;&#x67D0;&#x4E00;&#x6B65;&#x7684;&#x9009;&#x62E9;&#x662F;&#x597D;&#x8FD8;&#x662F;&#x574F;&#x3002;</td>
</tr>
<tr>
<td>&#x8F93;&#x5165;&#x7279;&#x5F81;</td>
<td>&#x8F93;&#x5165;&#x662F;<strong>&#x72EC;&#x7ACB;&#x540C;&#x5206;&#x5E03;</strong>&#x7684;&#x3002;</td>
<td>&#x9762;&#x5BF9;&#x7684;&#x8F93;&#x5165;&#x603B;&#x662F;&#x5728;&#x53D8;&#x5316;&#xFF0C;&#x6BCF;&#x5F53;&#x7B97;&#x6CD5;&#x505A;&#x51FA;&#x4E00;&#x4E2A;&#x884C;&#x4E3A;&#xFF0C;&#x5B83;&#x5F71;&#x54CD;&#x4E0B;&#x4E00;&#x6B21;&#x51B3;&#x7B56;&#x7684;&#x8F93;&#x5165;&#x3002;</td>
</tr>
</tbody>
</table>
<p>&#x62D3;&#x5C55;&#x6982;&#x5FF5;&#xFF1A;&#x4EC0;&#x4E48;&#x662F;<strong>&#x72EC;&#x7ACB;&#x540C;&#x5206;&#x5E03;</strong>&#xFF1A;</p>
<p><a href="../ReadingExtension/2.&#x72EC;&#x7ACB;&#x540C;&#x5206;&#x5E03;.html">&#x72EC;&#x7ACB;&#x540C;&#x5206;&#x5E03;&#x6982;&#x5FF5;</a></p>
<p><img src="../images/alpha_zero.gif" alt=""></p>
<p>&#x62D3;&#x5C55;&#x9605;&#x8BFB;&#xFF1A;Alphago&#x8FDB;&#x5316;&#x53F2; &#x6F2B;&#x753B;&#x544A;&#x8BC9;&#x4F60;Zero&#x4E3A;&#x4EC0;&#x4E48;&#x8FD9;&#x4E48;&#x725B;&#xFF1A;</p>
<p><a href="http://sports.sina.com.cn/chess/weiqi/2017-10-21/doc-ifymyyxw4023875.shtml" target="_blank">http://sports.sina.com.cn/chess/weiqi/2017-10-21/doc-ifymyyxw4023875.shtml</a></p>
<h2 id="5-&#x5C0F;&#x7ED3;">5 &#x5C0F;&#x7ED3;</h2>
<table>
<thead>
<tr>
<th style="text-align:left"></th>
<th style="text-align:center"><strong>In</strong></th>
<th style="text-align:center"><strong>Out</strong></th>
<th style="text-align:center"><strong>&#x76EE;&#x7684;</strong></th>
<th style="text-align:center"><strong>&#x6848;&#x4F8B;</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left"><strong>&#x76D1;&#x7763;&#x5B66;&#x4E60;</strong>   <br><strong>(supervised   learning)</strong></td>
<td style="text-align:center">&#x6709;&#x6807;&#x7B7E;</td>
<td style="text-align:center">&#x6709;&#x53CD;&#x9988;</td>
<td style="text-align:center">&#x9884;&#x6D4B;&#x7ED3;&#x679C;</td>
<td style="text-align:center">&#x732B;&#x72D7;&#x5206;&#x7C7B;   &#x623F;&#x4EF7;&#x9884;&#x6D4B;</td>
</tr>
<tr>
<td style="text-align:left"><strong>&#x65E0;&#x76D1;&#x7763;&#x5B66;&#x4E60;</strong>  <br> <strong>(unsupervised   learning)</strong></td>
<td style="text-align:center">&#x65E0;&#x6807;&#x7B7E;</td>
<td style="text-align:center">&#x65E0;&#x53CD;&#x9988;</td>
<td style="text-align:center">&#x53D1;&#x73B0;&#x6F5C;&#x5728;&#x7ED3;&#x6784;</td>
<td style="text-align:center">&#x201C;&#x7269;&#x4EE5;&#x7C7B;&#x805A;&#xFF0C;&#x4EBA;&#x4EE5;&#x7FA4;&#x5206;&#x201D;</td>
</tr>
<tr>
<td style="text-align:left"><strong>&#x534A;&#x76D1;&#x7763;&#x5B66;&#x4E60;</strong>   <br><strong>(Semi-Supervised   Learning)</strong></td>
<td style="text-align:center">&#x90E8;&#x5206;&#x6709;&#x6807;&#x7B7E;&#xFF0C;&#x90E8;&#x5206;&#x65E0;&#x6807;&#x7B7E;</td>
<td style="text-align:center">&#x6709;&#x53CD;&#x9988;</td>
<td style="text-align:center">&#x964D;&#x4F4E;&#x6570;&#x636E;&#x6807;&#x8BB0;&#x7684;&#x96BE;&#x5EA6;</td>
<td style="text-align:center"></td>
</tr>
<tr>
<td style="text-align:left"><strong>&#x5F3A;&#x5316;&#x5B66;&#x4E60;</strong>  <br> <strong>(reinforcement   learning)</strong></td>
<td style="text-align:center">&#x51B3;&#x7B56;&#x6D41;&#x7A0B;&#x53CA;&#x6FC0;&#x52B1;&#x7CFB;&#x7EDF;</td>
<td style="text-align:center">&#x4E00;&#x7CFB;&#x5217;&#x884C;&#x52A8;</td>
<td style="text-align:center">&#x957F;&#x671F;&#x5229;&#x76CA;&#x6700;&#x5927;&#x5316;</td>
<td style="text-align:center">&#x5B66;&#x4E0B;&#x68CB;</td>
</tr>
</tbody>
</table>

                    
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